Comparer des méthodes

	Pondération par l'inverse de la probabilité pour données de panel	Estimateur par appariement sur données de panel
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000	1997-2021
Auteur d'origine	Robins, Hernan & Brumback	Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension
Type	Reweighting / causal inference	Quasi-experimental causal estimator
Source fondatrice	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605-654.
Alias	panel IPW, longitudinal IPW, time-varying IPW, panel IPTW	panel matching, matching-on-panel-data, longitudinal matching estimator, PDME
Apparentées	5	6
Résumé	Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods.	The panel data matching estimator identifies causal treatment effects by pairing each treated unit with one or more control units that share similar covariate histories in the pre-treatment periods. By exploiting the longitudinal structure of panel data, it controls for both observed time-varying confounders and stable unit characteristics, estimating the average treatment effect on the treated (ATT) without requiring a parallel-trends assumption.